Application of fuzzy TOPSIS in evaluating sustainable transportation systems

Sustainable transportation systems are the need of modern times. There has been an unexpected growth in the number of transportation activities over years and the trend is expected to continue in the coming years. This has obviously associated environmental costs like air pollution, noise, etc. which is degrading the quality of life in modern cities. To cope us this crisis, municipal administrations are investing in sustainable transportation systems that are not only efficient, robust and economical but also friendly towards environment. The challenge before the transportation decision makers is how to evaluate and select such sustainable transportation systems. In this paper, we present a multicriteria decision making approach for selecting sustainability transportation systems under partial or incomplete information (uncertainty). The proposed approach comprises of three steps. In step 1, we identify the criteria for sustainability assessment of transportation. In step 2, experts provide linguistic ratings to the potential alternatives against the selected criteria. Fuzzy TOPSIS is used to generate aggregate scores for sustainability assessment and selection of best alternative. In step 3, sensitivity analysis is performed to determine the influence of criteria weights on the decision making process. A numerical illustration is provided to demonstrate the applicability of the approach.The strength of the proposed work is its practical applicability and the ability to generate good quality solutions under uncertainty.

► We present a multicriteria decision making approach for selecting sustainable transportation systems under partial or incomplete information (uncertainty).
► Fuzzy TOPSIS is used to generate aggregate scores for sustainability assessment and selection of best alternative.
► Sensitivity analysis is performed to determine the influence of criteria weights on the decision making process.
► The strength of the proposed approach is its ability to treat linguistic information in the absence of or under limited quantitative data.